arXiv subjects

John Zobolas

Publications and source records attributed to John Zobolas.

6 recordsLinked to original sources

arXiv2025

Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data

Prediction of patient survival using high-dimensional multi-omics data requires systematic feature selection methods that ensure predictive performance, sparsity, and reliability for prognostic biomarker discovery. We developed a hybrid ensemble feature selection (hEFS) approach that combines data subsampling with multiple prognostic models, integrating both embedded and wrapper-based strategies for survival prediction. Omics features are ranked using a voting-theory-inspired aggregation mechanism across models and subsamples, while the optimal number of features is selected via a Pareto front, balancing predictive accuracy and model sparsity without any user-defined thresholds. When applied to multi-omics datasets from three pancreatic cancer cohorts, hEFS identifies significantly fewer and more stable biomarkers compared to the conventional, late-fusion CoxLasso models, while maintaining comparable discrimination performance. Implemented within the open-source mlr3fselect R package, hEFS offers a robust, interpretable, and clinically valuable tool for prognostic modelling and biomarker discovery in high-dimensional survival settings.

q-bio.GN↗

arXiv2025

Reduction Techniques for Survival Analysis

In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that "reduce" a survival task to a more common regression or classification task, without ignoring the specifics of survival data. Such techniques particularly facilitate machine learning-based survival analysis, as they allow for applying standard tools from machine and deep learning to many survival tasks without requiring custom learners. We provide an overview of different reduction techniques and discuss their respective strengths and weaknesses. We also provide a principled implementation of some of these reductions, such that they are directly available within standard machine learning workflows. We illustrate each reduction using dedicated examples and perform a benchmark analysis that compares their predictive performance to established machine learning methods for survival analysis.

stat.ML↗

arXiv2024

A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data

This work presents the first large-scale neutral benchmark experiment focused on single-event, right-censored, low-dimensional survival data. Benchmark experiments are essential in methodological research to scientifically compare new and existing model classes through proper empirical evaluation. Existing benchmarks in the survival literature are smaller in scale regarding the number of used datasets and extent of empirical evaluation. They often lack appropriate tuning or evaluation procedures, while other comparison studies focus on qualitative reviews rather than quantitative comparisons. This comprehensive study aims to fill the gap by neutrally evaluating a broad range of methods and providing generalizable guidelines for practitioners. We benchmark 19 models, ranging from classical statistical approaches to many common machine learning methods, on 34 publicly available datasets. The benchmark tunes models using both a discrimination measure (Harrell's C-index) and a scoring rule (Integrated Survival Brier Score), and evaluates them across six metrics covering discrimination, calibration, and overall predictive performance. Despite superior average ranks in overall predictive performance from individual learners like oblique random survival forests and likelihood-based boosting, and better discrimination rankings from multiple boosting- and tree-based methods as well as parametric survival models, no method significantly outperforms the commonly used Cox proportional hazards model for either tuning measure. We conclude that for predictive purposes in the standard survival analysis setting of low-dimensional, right-censored data, the Cox Proportional Hazards model remains a simple and robust method, sufficient for most practitioners. All code, data, and results are publicly available on GitHub https://github.com/slds-lmu/paper_2023_survival_benchmark

stat.ML↗

arXiv2023

Tutorial on survival modeling with applications to omics data

Motivation: Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics data sets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes. Results: We present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are are especially useful for high-dimensional data, but the framework is applicable more generally. Availability and implementation: A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics/survomics.html.

stat.AP↗

arXiv2022

When Are Scoring Rules Proper? Bridging Theory and Practice in Survival Model Evaluation

Proper scoring rules encourage probabilistic predictions that match the true underlying distribution and are central to model evaluation, with increasing relevance in automated workflows such as AutoML. In survival analysis, however, their behavior under censoring is not fully understood. We study commonly used squared and logarithmic scoring rules for right-censored survival data under independent censoring, introducing a notion of marginal properness based on observable outcomes. Within this framework, we show that the SBS, evaluated at a fixed time point, along with its integrated version (ISBS) and the RCLL are strictly proper when all individuals eventually experience the event, but can become improper under finite follow-up or in the presence of cure fractions. For the SBS, we derive a closed-form expression that reveals the true mechanism: residual mass, corresponding to individuals who remain event-free at study end, systematically biases the score toward underestimating survival, with the effect increasing at later evaluation times and under heavier censoring. Through simulation experiments, we examine how these issues manifest in finite samples and under misspecification. The SBS exhibits pronounced improperness at late evaluation times and poor discrimination between models. The ISBS is more robust due to temporal integration but remains sensitive to tail regularity violations, exhibiting detectable improperness and reduced discriminatory power. The RCLL behaves consistently with strict properness and effectively separates misspecified models. Overall, our results demonstrate how theoretical improperness can translate into misleading model comparisons, underscoring the need for further methodological development in survival model evaluation under censoring and realistic data conditions.

math.ST↗

arXiv2021

Boolean function metrics can assist modelers to check and choose logical rules

Computational models of biological processes provide one of the most powerful methods for a detailed analysis of the mechanisms that drive the behavior of complex systems. Logic-based modeling has enhanced our understanding and interpretation of those systems. Defining rules that determine how the output activity of biological entities is regulated by their respective inputs has proven to be challenging, due to increasingly larger models and the presence of noise in data, allowing multiple model parameterizations to fit the experimental observations. We present several Boolean function metrics that provide modelers with the appropriate framework to analyze the impact of a particular model parameterization. We demonstrate the link between a semantic characterization of a Boolean function and its consistency with the model's underlying regulatory structure. We further define the properties that outline such consistency and show that several of the Boolean functions under study violate them, questioning their biological plausibility and subsequent use. We also illustrate that regulatory functions can have major differences with regard to their asymptotic output behavior, with some of them being biased towards specific Boolean outcomes when others are dependent on the ratio between activating and inhibitory regulators. Application results show that in a specific signaling cancer network, the function bias can be used to guide the choice of logical operators for a model that matches data observations. Moreover, graph analysis indicates that the standardized Boolean function bias becomes more prominent with increasing numbers of regulators, confirming the fact that rule specification can effectively determine regulatory outcome despite the complex dynamics of biological networks.

q-bio.MN↗